Research Proposal

Bhaswar Chakma

2025-05-20

  • In 2018, The Economist named her one of the decade’s eight best young economists.

  • In 2014 she was named by the IMF as the youngest of 25 economists under the age of 45 shaping thought about the global economy.

What is ESG and Why It Matters

  • E: Environmental — climate risk, emissions, energy use
  • S: Social — labor rights, diversity, community impact
  • G: Governance — board structure, executive compensation, ethics
  • ESG evaluates non-financial risk and ethical standards
  • Relevance: ESG data is unstructured and strategic → ideal for ML and causal inference

Overview

Three empirical papers on ESG and financial markets:

  1. ESG Disclosure and Price Informativeness
    → Do disclosures improve market efficiency?

  2. ESG in Asset Pricing with Deep Learning
    → Do ESG features improve stock return forecasts?

  3. Institutional ESG Consistency
    → Do institutions align ESG rhetoric with their actions?

All papers combine machine learning and causal inference with financial and text data.

Paper 1 – Goals and Motivation

Main Goal:
To examine whether ESG disclosures improve the efficiency of financial markets.

Key Questions:
- Do ESG disclosures lead to lower return volatility?
- Do they reduce bid-ask spreads?
- Do they accelerate price discovery?

Why It Matters:
- ESG reporting is increasing but varies in quality.
- Prior studies focus on the presence of disclosure, not the content.
- Investors and regulators need to know what kind of ESG reporting actually informs markets.

Paper 1 – What I Will Do

  • Collect ESG disclosure text from 10-Ks and sustainability reports
  • Use BERT/RoBERTa to extract:
    • Sentiment, specificity, readability, topical focus
  • Apply contrastive learning to detect boilerplate vs meaningful content
  • Estimate market effects: volatility, spreads, price delay
  • Identify causal effects using IV (e.g., regulatory changes)
  • Combine ML with inference using prediction-powered methods

Paper 1 – Expected Results

Expected Results:
- Forward-looking, specific ESG disclosures will reduce:

  • Return volatility
  • Bid-ask spreads
  • Price delay

Why?
- Christensen, Serafeim, & Sikochi (2022):
High-quality ESG disclosures improve liquidity and transparency.
→ Content quality, not just disclosure presence, drives outcomes.

Paper 1 – My Contribution

  • Apply transformer NLP (BERT) to ESG reporting content
  • Use contrastive learning to evaluate substance vs boilerplate
  • Estimate causal effects using IV and prediction-powered inference [Zrnic and Candès (2023)]
  • Advance ESG market microstructure research using text-based methods

Paper 2 – Goals and Motivation

Main Goal:
To test whether ESG features improve stock return prediction in a machine learning framework.

Key Questions:
- Can ESG data improve asset pricing models?
- Are ESG-informed portfolios more profitable and stable?

Why It Matters:
- Traditional models (e.g., Fama-French) assume linearity.
- ESG data is high-dimensional, likely nonlinear, and underused.
- ML has improved pricing models (Gu et al., 2020) but rarely includes ESG.

Paper 2 – What I Will Do

  • Build firm-month panel: returns, fundamentals, ESG (CRSP, Compustat, Bloomberg, LSEG)
  • Apply ML models: Lasso, XGBoost, Neural Networks
  • Add ESG features: emissions, board diversity, controversies
  • Evaluate model performance: out-of-sample R², Sharpe ratio, turnover
  • Interpret models using SHAP values and PDPs

Paper 2 – Expected Results

Expected Results:
- ESG features will enhance predictive power
- ESG-augmented models will yield:
- Higher Sharpe ratios
- Better R²
- Lower turnover

Why?
- Gu, Kelly, & Xiu (2020):
ML models outperform linear ones in return prediction.
→ Adding ESG may reveal alpha related to risk or preferences.

Paper 2 – My Contribution

  • First study integrating ESG into full ML asset pricing pipeline
  • Demonstrates ESG alpha using interpretable ML
  • Bridges predictive power and economic interpretation
  • Offers practical tools for investors managing ESG portfolios

Paper 3 – Goals and Motivation

Main Goal:
To assess whether institutional investors align ESG claims with their real behavior.

Key Questions:
- Do ESG statements match actual voting and portfolio decisions?
- Do inconsistencies affect investor flows?

Why It Matters:
- ESG credibility is under fire — greenwashing is a top concern
- No scalable method exists to measure ESG consistency
- Investor trust depends on aligning talk and action

Paper 3 – What I Will Do

  • Collect ESG policy statements, proxy votes (N-PX), and holdings (13F)
  • Apply transformer NLP to extract ESG pledges by theme
  • Link actions to claims using LinkTransformer + HDBSCAN
  • Build ESG Consistency Index
  • Use DiD regressions to test effect on fund flows (CRSP)

Paper 3 – Expected Results

Expected Results:
- Many funds will show inconsistencies across statements, votes, and holdings
- Inconsistent funds will lose capital from ESG-conscious investors

Why?
- Raghunandan & Rajgopal (2022):
ESG-branded funds often vote against ESG proposals.
→ This disconnect signals symbolic rather than substantive commitment.

Paper 3 – My Contribution

  • Create the first scalable ESG Consistency Index
  • Use NLP + record linkage + clustering to evaluate ESG behavior
  • Quantify the financial consequences of ESG inconsistency
  • Help investors and regulators hold ESG funds accountable